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Boosting Probabilistic Graphical Model Inference by Incorporating Prior Knowledge from Multiple Sources
Inferring regulatory networks from experimental data via probabilistic graphical models is a popular framework to gain insights into biological systems. However, the inherent noise in experimental data coupled with a limited sample size reduces the performance of network reverse engineering. Prior k...
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| Hlavní autoři: | , |
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| Médium: | Artigo |
| Jazyk: | Inglês |
| Vydáno: |
Public Library of Science
2013
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| Témata: | |
| On-line přístup: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3691143/ https://ncbi.nlm.nih.gov/pubmed/23826291 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0067410 |
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